Expertise in colonoscopy intubation does not predict diagnostic accuracy: a simulation-based study

Background and study aims Studies have linked cecal intubation rate with adenoma detection rate; however, the direct association between technical performance during colonoscopy intubation and withdrawal has never been explored. Thus, it remains unclear whether gentle and efficient intubation predicts superior mucosal inspection. The aim of this study was to investigate the correlation between performance during intubation and withdrawal in a simulation-based setup. Methods Twenty-four physicians with various experience in colonoscopy performed twice on the Endoscopy Training System (ETS). Intubation skills were evaluated by assessing tasks on the ETS related to intubation (scope manipulation and loop management) and use of a computerized assessment tool called the 3D-Colonoscopy Progression Score (3D-CoPS). Diagnostic accuracy was defined by the number of polyps found during the ETS task of mucosal inspection. Pearson’s correlations were calculated to explore associations between intubation skill and diagnostic accuracy. Results The correlation analysis between 3D-CoPS and number of polyps found during mucosal inspection revealed a weak and insignificant correlation (0.157, P = 0.3). Likewise, an insignificant correlation was seen between ETS intubation and number of polyps found (0.149, P = 0.32). Conclusions We found no evidence to support that technical performance during intubation is correlated with mucosal inspection performance in a simulation-based setting.

Evaluation of Influence Factors on the Visual Inspection Performance of Aircraft Engine Blades

Background—There are various influence factors that affect visual inspection of aircraft engine blades including type of inspection, defect type, severity level, blade perspective and background colour. The effect of those factors on the inspection performance was assessed. Method—The inspection accuracy of fifty industry practitioners was measured for 137 blade images, leading to N = 6850 observations. The data were statistically analysed to identify the significant factors. Subsequent evaluation of the eye tracking data provided additional insights into the inspection process. Results—Inspection accuracies in borescope inspections were significantly lower compared to piece-part inspection at 63.8% and 82.6%, respectively. Airfoil dents (19.0%), cracks (11.0%), and blockage (8.0%) were the most difficult defects to detect, while nicks (100.0%), tears (95.5%), and tip curls (89.0%) had the highest detection rates. The classification accuracy was lowest for airfoil dents (5.3%), burns (38.4%), and tears (44.9%), while coating loss (98.1%), nicks (90.0%), and blockage (87.5%) were most accurately classified. Defects of severity level S1 (72.0%) were more difficult to detect than increased severity levels S2 (92.8%) and S3 (99.0%). Moreover, visual perspectives perpendicular to the airfoil led to better inspection rates (up to 87.5%) than edge perspectives (51.0% to 66.5%). Background colour was not a significant factor. The eye tracking results of novices showed an unstructured search path, characterised by numerous fixations, leading to longer inspection times. Experts in contrast applied a systematic search strategy with focus on the edges, and showed a better defect discrimination ability. This observation was consistent across all stimuli, thus independent of the influence factors. Conclusions—Eye tracking identified the challenges of the inspection process and errors made. A revised inspection framework was proposed based on insights gained, and support the idea of an underlying mental model.

Comparison of Visual and Visual–Tactile Inspection of Aircraft Engine Blades

Background—In aircraft engine maintenance, the majority of parts, including engine blades, are inspected visually for any damage to ensure a safe operation. While this process is called visual inspection, there are other human senses encompassed in this process such as tactile perception. Thus, there is a need to better understand the effect of the tactile component on visual inspection performance and whether this effect is consistent for different defect types and expertise groups. Method—This study comprised three experiments, each designed to test different levels of visual and tactile abilities. In each experiment, six industry practitioners of three expertise groups inspected the same sample of N = 26 blades. A two-week interval was allowed between the experiments. Inspection performance was measured in terms of inspection accuracy, inspection time, and defect classification accuracy. Results—The results showed that unrestrained vision and the addition of tactile perception led to higher inspection accuracies of 76.9% and 84.0%, respectively, compared to screen-based inspection with 70.5% accuracy. An improvement was also noted in classification accuracy, as 39.1%, 67.5%, and 79.4% of defects were correctly classified in screen-based, full vision and visual–tactile inspection, respectively. The shortest inspection time was measured for screen-based inspection (18.134 s) followed by visual–tactile (22.140 s) and full vision (25.064 s). Dents benefited the most from the tactile sense, while the false positive rate remained unchanged across all experiments. Nicks and dents were the most difficult to detect and classify and were often confused by operators. Conclusions—Visual inspection in combination with tactile perception led to better performance in inspecting engine blades than visual inspection alone. This has implications for industrial training programmes for fault detection.

Assessment of the Effect of Cleanliness on the Visual Inspection of Aircraft Engine Blades: An Eye Tracking Study

Background—The visual inspection of aircraft parts such as engine blades is crucial to ensure safe aircraft operation. There is a need to understand the reliability of such inspections and the factors that affect the results. In this study, the factor ‘cleanliness’ was analysed among other factors. Method—Fifty industry practitioners of three expertise levels inspected 24 images of parts with a variety of defects in clean and dirty conditions, resulting in a total of N = 1200 observations. The data were analysed statistically to evaluate the relationships between cleanliness and inspection performance. Eye tracking was applied to understand the search strategies of different levels of expertise for various part conditions. Results—The results show an inspection accuracy of 86.8% and 66.8% for clean and dirty blades, respectively. The statistical analysis showed that cleanliness and defect type influenced the inspection accuracy, while expertise was surprisingly not a significant factor. In contrast, inspection time was affected by expertise along with other factors, including cleanliness, defect type and visual acuity. Eye tracking revealed that inspectors (experts) apply a more structured and systematic search with less fixations and revisits compared to other groups. Conclusions—Cleaning prior to inspection leads to better results. Eye tracking revealed that inspectors used an underlying search strategy characterised by edge detection and differentiation between surface deposits and other types of damage, which contributed to better performance.

Use of Synthetic Flaws to Assess Pipeline Seam Weld Inspection Performance

Pipeline seam welds are often inspected using ultrasonic In-Line Inspection (ILI) technologies. The measurement performance specification of an ultrasonic ILI tool is based on simple, planar, machined notches which are very reproducible, but are not representative of the complex flaw morphologies that occur naturally in seams such as hook cracks and tilted lack of fusion flaws. In order to assess ILI performance on naturally occurring flaws, “in-the-ditch” Nondestructive Testing (ITD NDT) is performed to validate a subset of the population of ILI reported features. Due to the limited number, type, and dimensional (height and length) uncertainty of these flaws, the field validation approach has limitations in terms of efficiency and accuracy in determining ILI detection capabilities and sizing performance. Recently, specialized synthetic flaw fabrication technology has been developed and provides complex, natural crack-like morphologies with reliable and reproducible size dimensions. Effective validation spools with flaws (of representative geometries) can be achieved through engineered designs that consider the number, size and shape of manufactured flaws. This enables owners to quickly and reliably assess the performance of both ILI tools and ITD NDT operators. Assessing performance with the synthetic flaw approach provides results that are more comprehensive and cost-effective compared to the typical field validation approach alone. This is because the flaw population is designed rather than randomly selected from excavation data. This paper addresses the design, use and field experience with validation spools. This paper will present the performance of ILI tools and UT examiners based on synthetic flaw qualification exams, and how this supports related ILI and operator validation work.

Analysis of the Region of Interest According to CNN Structure in Hierarchical Pattern Surface Inspection Using CAM

A convolutional neural network (CNN), which exhibits excellent performance in solving image-based problem, has been widely applied to various industrial problems. In general, the CNN model was applied to defect inspection on the surface of raw materials or final products, and its accuracy also showed better performance compared to human inspection. However, surfaces with heterogeneous and complex backgrounds have difficulties in separating defects region from the background, which is a typical challenge in this field. In this study, the CNN model was applied to detect surface defects on a hierarchical patterned surface, one of the representative complex background surfaces. In order to optimize the CNN structure, the change in inspection performance was analyzed according to the number of layers and kernel size of the model using evaluation metrics. In addition, the change of the CNN’s decision criteria according to the change of the model structure was analyzed using a class activation map (CAM) technique, which can highlight the most important region recognized by the CNN in performing classification. As a result, we were able to accurately understand the classification manner of the CNN for the hierarchical pattern surface, and an accuracy of 93.7% was achieved using the optimized model.

Novel array ECT probe with three-phase excitation for steam generator tube inspection

Eddy current testing (ECT) is commonly used in steam generator tube inspection. Among multiple probes applied in this area, array probe shows the merit of the capability of obtaining a C-scan image in a simple linear scan. In this work, a novel array ECT probe with three-phase excitation is proposed. The probe consists of two rows of excitation coils and a row of pickup coils. The induced eddy current shifts electrically in the testing sample so that the necessity of multiplexer is eliminated, resulting in advantages of lower cost, less noise and faster inspection speed. Theoretical analysis indicates that the background signal of the array sensor would be small. A 3D finite element method (FEM) model is developed to study the operating principle and to predict the inspection performance of the probe. A prototype probe has been developed and tested, by which a steam generator tube sample with different kinds of defects was inspected. The experiment results validate the simulation model and further demonstrate the feasibility of the sensor.

Automatic optical inspection system for IC solder joint based on local-to-global ensemble learning

Purpose Automatic optical inspection (AOI) systems have been widely used in many fields to evaluate the qualities of products at the end of the production line. The purpose of this paper is to propose a local-to-global ensemble learning method for the AOI system based on to inspect integrated circuit (IC) solder joints defects. Design/methodology/approach In the proposed method, the locally statistically modeling stage and the globally ensemble learning stage are involved to tackle the inspection problem. At the former stage, the improved visual background extraction–based algorithm is used for locally statistically modeling to grasp tiny appearance differences between the IC solder joints to achieve potential defect images for the subsequent stage. At the latter stage, mean unqualified probability is introduced based on a novel ensemble learning, in which an adaptive weighted strategy is proposed for revealing different contributions of the base classifier to the inspection performance. Findings Experimental results demonstrate that the proposed method achieves better inspection performance with an acceptable inspection time compared with some state-of-the-art methods. Originality/value The approach is a promising method for IC solder joint inspection, which can simultaneously grasp the local characteristics of IC solder joints and reveal inherently global relationships between IC solder joints.

Defect Inspection in Display Panel Using Concentrated Auto Encoder

In this paper, concentrated auto encoder (CAE) is proposed for aligning photo spacer (PS) and for local inspection of PS. The CAE method has two characteristics. First, unaligned images can be moved to the same alignment position, which makes it possible to move the measured PS images to the same position in order to directly compare the images. Second, the characteristics of the abnormal PS are maintained even if the PS is aligned by the CAE method. The abnormal PS obtained through CAE has the same alignment as the reference PS and has its abnormal characteristics. The presence or absence of defects and the location of defects were identified without precisely measuring the height of the PS and critical dimension (CD). Also, alignment and defect inspection were performed simultaneously, which shortened the inspection time. Finally, inspection performance parameters and inspection time were analyzed to confirm the validity of the CAE method and were compared with the image similarity comparison methods used for defect inspection.